Hyperspectral　unmixing　is　one　of　the　most　important　procedures　for　remote　sensing　image　processing.　The　non-negative　matrix　factorization　(NMF)-based　method　has　been　widely　used　for　hyperspectral　unmixing　since　it　can　get　endmember　and　abundance　matrices　simultaneously.　However,　the　inherent　single-decomposition　structure　of　NMF　may　not　achieve　good　performance　for　highly　mixed　data.　To　solve　this　issue,　we　propose　a　spectral　and　spatial　total-variation-regularized　multilayer　non-negative　matrix　factorization　(SSTV-MLNMF)　for　hyperspectral　unmixing.　In　SSTV-MLNMF,　we　designed　an　effective　multilayer　factorization　process　and　combined　spectral　and　spatial　total　variation　as　extra　regularization.　These　could　enhance　the　smoothness　for　spectral　signatures　and　spatial　fields,　which　could　achieve　better　performance.　Experiments　on　both　synthetic　and　real　datasets　have　validated　the　effectiveness　of　our　method　and　have　shown　that　it　has　outperformed　several　state-of-the-art　approaches　of　hyperspectral　unmixing.　(C)　2019　Society　of　Photo-Optical　Instrumentation　Engineers　(SPIE)